Abstract

We sought to determine whether the detection and the identification of texture modulations are mediated by a common mechanism. On each trial two textures were presented, one of which contained a modulation in orientation (OM), spatial frequency (FM), or contrast (CM). Observers were required to indicate whether the modulated texture was presented in the first or the second interval as well as the nature of the texture modulation. The results showed that for two of the three pairwise matchings (OM–FM and OM–CM) detection and identification performance were nearly identical, suggesting a common underlying mechanism. However, when FM and CM textures were paired, discrimination thresholds were significantly higher than detection thresholds. In the context of the filter–rectify–filter model of texture perception, our results suggest that the mechanisms underlying detection are labeled with respect to their first-order input; i.e., the identities of these mechanisms are available to higher levels of processing. Several possible explanations for the misidentification of FM and CM at detection threshold are considered.

We model the spectral amplitude distribution here as H(f, θ)=exp(-0.5[(f-f0)/(f0σf)]2)exp(-0.5[(θ-θ0)/σθ]2), where f is frequency, f0 is the dc spatial frequency (5 cpd), σf is a constant determining spatial-frequency bandwidth and set at a value of 0.41, θ is orientation, θ0 is the dc orientation of the texture (0°, horizontal), and σθ is a constant determining orientation bandwidth and set at 25.5. This function describes the average spectral content of the textures used here quite well.14

The statistical test employed to assess goodness of fit indicated a poor fit for the identification curve of NP in the OM–CM condition (p<0.01). However, as the reader may verify by inspection of Fig. 4, the data appear to fit the curve quite well. As it turns out, the poor goodness of fit is due almost entirely to one data point, namely, identification performance for OM textures at the second-highest value of modulation amplitude. This suggests that the poor fit is likely a spurious result. The p value of the deviance score calculated when this data point is omitted is 0.57.

Other

We model the spectral amplitude distribution here as H(f, θ)=exp(-0.5[(f-f0)/(f0σf)]2)exp(-0.5[(θ-θ0)/σθ]2), where f is frequency, f0 is the dc spatial frequency (5 cpd), σf is a constant determining spatial-frequency bandwidth and set at a value of 0.41, θ is orientation, θ0 is the dc orientation of the texture (0°, horizontal), and σθ is a constant determining orientation bandwidth and set at 25.5. This function describes the average spectral content of the textures used here quite well.14

The statistical test employed to assess goodness of fit indicated a poor fit for the identification curve of NP in the OM–CM condition (p<0.01). However, as the reader may verify by inspection of Fig. 4, the data appear to fit the curve quite well. As it turns out, the poor goodness of fit is due almost entirely to one data point, namely, identification performance for OM textures at the second-highest value of modulation amplitude. This suggests that the poor fit is likely a spurious result. The p value of the deviance score calculated when this data point is omitted is 0.57.

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